The clinical and imaging data fusion model for single-period cerebral CTA collateral circulation assessment.

IF 1.7 3区 医学 Q3 INSTRUMENTS & INSTRUMENTATION Journal of X-Ray Science and Technology Pub Date : 2024-01-01 DOI:10.3233/XST-240083
Yuqi Ma, Jingliu He, Duo Tan, Xu Han, Ruiqi Feng, Hailing Xiong, Xihua Peng, Xun Pu, Lin Zhang, Yongmei Li, Shanxiong Chen
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Abstract

Background: The Chinese population ranks among the highest globally in terms of stroke prevalence. In the clinical diagnostic process, radiologists utilize computed tomography angiography (CTA) images for diagnosis, enabling a precise assessment of collateral circulation in the brains of stroke patients. Recent studies frequently combine imaging and machine learning methods to develop computer-aided diagnostic algorithms. However, in studies concerning collateral circulation assessment, the extracted imaging features are primarily composed of manually designed statistical features, which exhibit significant limitations in their representational capacity. Accurately assessing collateral circulation using image features in brain CTA images still presents challenges.

Methods: To tackle this issue, considering the scarcity of publicly accessible medical datasets, we combined clinical data with imaging data to establish a dataset named RadiomicsClinicCTA. Moreover, we devised two collateral circulation assessment models to exploit the synergistic potential of patients' clinical information and imaging data for a more accurate assessment of collateral circulation: data-level fusion and feature-level fusion. To remove redundant features from the dataset, we employed Levene's test and T-test methods for feature pre-screening. Subsequently, we performed feature dimensionality reduction using the LASSO and random forest algorithms and trained classification models with various machine learning algorithms on the data-level fusion dataset after feature engineering.

Results: Experimental results on the RadiomicsClinicCTA dataset demonstrate that the optimized data-level fusion model achieves an accuracy and AUC value exceeding 86%. Subsequently, we trained and assessed the performance of the feature-level fusion classification model. The results indicate the feature-level fusion classification model outperforms the optimized data-level fusion model. Comparative experiments show that the fused dataset better differentiates between good and bad side branch features relative to the pure radiomics dataset.

Conclusions: Our study underscores the efficacy of integrating clinical and imaging data through fusion models, significantly enhancing the accuracy of collateral circulation assessment in stroke patients.

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用于单周期脑 CTA 侧支循环评估的临床和成像数据融合模型。
背景:中国是全球脑卒中发病率最高的国家之一。在临床诊断过程中,放射科医生利用计算机断层血管造影(CTA)图像进行诊断,从而精确评估脑卒中患者脑部的侧支循环。最近的研究经常将成像和机器学习方法结合起来,开发计算机辅助诊断算法。然而,在有关侧支循环评估的研究中,提取的成像特征主要由人工设计的统计特征组成,在表征能力上有很大的局限性。利用脑 CTA 图像中的图像特征准确评估侧支循环仍是一项挑战:为了解决这个问题,考虑到可公开访问的医学数据集的稀缺性,我们将临床数据与成像数据相结合,建立了一个名为 RadiomicsClinicCTA 的数据集。此外,我们还设计了两种侧支循环评估模型,以利用患者临床信息和影像数据的协同潜力,更准确地评估侧支循环:数据级融合和特征级融合。为了去除数据集中的冗余特征,我们采用了 Levene 检验和 T 检验方法进行特征预筛选。随后,我们使用 LASSO 和随机森林算法进行特征降维,并在特征工程后的数据级融合数据集上使用各种机器学习算法训练分类模型:在RadiomicsClinicCTA数据集上的实验结果表明,优化后的数据级融合模型的准确率和AUC值均超过86%。随后,我们训练并评估了特征级融合分类模型的性能。结果表明,特征级融合分类模型优于优化的数据级融合模型。对比实验表明,与纯放射组学数据集相比,融合后的数据集能更好地区分好侧枝和坏侧枝特征:我们的研究强调了通过融合模型整合临床和影像学数据的功效,大大提高了脑卒中患者侧支循环评估的准确性。
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来源期刊
CiteScore
4.90
自引率
23.30%
发文量
150
审稿时长
3 months
期刊介绍: Research areas within the scope of the journal include: Interaction of x-rays with matter: x-ray phenomena, biological effects of radiation, radiation safety and optical constants X-ray sources: x-rays from synchrotrons, x-ray lasers, plasmas, and other sources, conventional or unconventional Optical elements: grazing incidence optics, multilayer mirrors, zone plates, gratings, other diffraction optics Optical instruments: interferometers, spectrometers, microscopes, telescopes, microprobes
期刊最新文献
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